{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Radiation Pattern of an Antenna"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we compute the radiation pattern of an antenna. This involves an electric-current point dipole emitter in vacuum. The source is placed at the center of a 2d cell surrounded by PML. The near fields are obtained on a bounding box defined along the edges of the non-PML region. The far fields are computed in two ways from closed surfaces: (1) sides of a square and (2) circumference of a circle, having a length/radius many times larger than the source wavelength and lying beyond the cell. From both the near and far fields, we will also compute the total outgoing Poynting flux and demonstrate that they are equivalent. Results will be shown for three orthogonal polarizations of the input source.\n",
    "\n",
    "The simulation geometry is shown in the following schematic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![geometry](https://meep.readthedocs.io/en/latest/images/Near2far_simulation_geometry.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the first part of the simulation, we define the cell and source as well as the near field and flux regions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using MPI version 3.1, 1 processes\n"
     ]
    }
   ],
   "source": [
    "import meep as mp\n",
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "resolution = 50  # pixels/μm\n",
    "\n",
    "sxy = 4\n",
    "dpml = 1\n",
    "cell = mp.Vector3(sxy+2*dpml,sxy+2*dpml)\n",
    "\n",
    "pml_layers = [mp.PML(dpml)]\n",
    "\n",
    "fcen = 1.0\n",
    "df = 0.4\n",
    "src_cmpt = mp.Ey\n",
    "sources = [mp.Source(src=mp.GaussianSource(fcen,fwidth=df),\n",
    "                    center=mp.Vector3(),\n",
    "                    component=src_cmpt)]\n",
    "\n",
    "if src_cmpt == mp.Ex:\n",
    "    symmetries = [mp.Mirror(mp.X,phase=-1),\n",
    "                  mp.Mirror(mp.Y,phase=+1)]\n",
    "elif src_cmpt == mp.Ey:\n",
    "    symmetries = [mp.Mirror(mp.X,phase=+1),\n",
    "                  mp.Mirror(mp.Y,phase=-1)]\n",
    "elif src_cmpt == mp.Ez:\n",
    "    symmetries = [mp.Mirror(mp.X,phase=+1),\n",
    "                  mp.Mirror(mp.Y,phase=+1)]\n",
    "\n",
    "sim = mp.Simulation(cell_size=cell,\n",
    "                    resolution=resolution,\n",
    "                    sources=sources,\n",
    "                    symmetries=symmetries,\n",
    "                    boundary_layers=pml_layers)\n",
    "\n",
    "nearfield_box = sim.add_near2far(fcen, 0, 1,\n",
    "                                 mp.Near2FarRegion(mp.Vector3(y=0.5*sxy), size=mp.Vector3(sxy)),\n",
    "                                 mp.Near2FarRegion(mp.Vector3(y=-0.5*sxy), size=mp.Vector3(sxy), weight=-1),\n",
    "                                 mp.Near2FarRegion(mp.Vector3(0.5*sxy), size=mp.Vector3(y=sxy)),\n",
    "                                 mp.Near2FarRegion(mp.Vector3(-0.5*sxy), size=mp.Vector3(y=sxy), weight=-1))\n",
    "\n",
    "flux_box = sim.add_flux(fcen, 0, 1,\n",
    "                        mp.FluxRegion(mp.Vector3(y=0.5*sxy), size=mp.Vector3(sxy)),\n",
    "                        mp.FluxRegion(mp.Vector3(y=-0.5*sxy), size=mp.Vector3(sxy), weight=-1),\n",
    "                        mp.FluxRegion(mp.Vector3(0.5*sxy), size=mp.Vector3(y=sxy)),\n",
    "                        mp.FluxRegion(mp.Vector3(-0.5*sxy), size=mp.Vector3(y=sxy), weight=-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize our simulation domain to ensure everything is correct:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Initializing structure...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(dpi=150)\n",
    "sim.plot2D(ax=f.gca())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are using a pulsed source (with center wavelength of 1 $\\mu$m), the fields are timestepped until they have sufficiently decayed away."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "field decay(t = 50.01): 4938.653280565278 / 4938.653280565278 = 1.0\n",
      "field decay(t = 100.01): 7.375043707445242e-11 / 4938.653280565278 = 1.4933309322336344e-14\n",
      "run 0 finished at t = 100.01 (10001 timesteps)\n"
     ]
    }
   ],
   "source": [
    "sim.run(until_after_sources=mp.stop_when_fields_decayed(50, src_cmpt, mp.Vector3(), 1e-8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After the time stepping, the flux of the near fields is computed using `get_fluxes`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "near_flux = mp.get_fluxes(flux_box)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the first of two cases, the flux of the far fields is computed using the `flux` routine for a square box of side length 2 mm which is 2000 times larger than the source wavelength. This requires computing the outgoing flux on each of the four sides of the box separately and summing the values. The resolution of the far fields is chosen arbitrarily as 1 point/$\\mu$m. This means there are 2x106 points per side length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = 1000/fcen      # half side length of far-field square box OR radius of far-field circle\n",
    "res_ff = 1         # resolution of far fields (points/μm)\n",
    "far_flux_box = (nearfield_box.flux(mp.Y, mp.Volume(center=mp.Vector3(y=r), size=mp.Vector3(2*r)), res_ff)[0]\n",
    "               - nearfield_box.flux(mp.Y, mp.Volume(center=mp.Vector3(y=-r), size=mp.Vector3(2*r)), res_ff)[0]\n",
    "               + nearfield_box.flux(mp.X, mp.Volume(center=mp.Vector3(r), size=mp.Vector3(y=2*r)), res_ff)[0]\n",
    "               - nearfield_box.flux(mp.X, mp.Volume(center=mp.Vector3(-r), size=mp.Vector3(y=2*r)), res_ff)[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the second of two cases, we use the `get_farfield` routine to compute the far fields by looping over a set of 100 equally-spaced points along the circumference of a circle with radius of 1 mm. The six far field components ($E_x$, $E_y$, $E_z$, $H_x$, $H_y$, $H_z$) are stored as separate arrays of complex numbers. From the far fields at each point $r$, we compute the outgoing or radial flux: $P^2_x+P^2_y$, where $P_x$ and $P_y$ are the components of the Poynting vector $P(r)=(P_x,P_y,P_z)=\\Re(E(r) \\times H(r))$. Note that $P_z$ is always 0 since this is a 2d simulation. The total flux is computed and the three flux values are displayed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "flux:, 1.227787, 1.227651, 1.227260\n"
     ]
    }
   ],
   "source": [
    "npts = 100         # number of points in [0,2*pi) range of angles\n",
    "angles = 2*math.pi/npts*np.arange(npts)\n",
    "\n",
    "E = np.zeros((npts,3),dtype=np.complex128)\n",
    "H = np.zeros((npts,3),dtype=np.complex128)\n",
    "for n in range(npts):\n",
    "    ff = sim.get_farfield(nearfield_box,\n",
    "                          mp.Vector3(r*math.cos(angles[n]),\n",
    "                                     r*math.sin(angles[n])))\n",
    "    E[n,:] = [np.conj(ff[j]) for j in range(3)]\n",
    "    H[n,:] = [ff[j+3] for j in range(3)]\n",
    "\n",
    "Px = np.real(np.multiply(E[:,1],H[:,2])-np.multiply(E[:,2],H[:,1]))\n",
    "Py = np.real(np.multiply(E[:,2],H[:,0])-np.multiply(E[:,0],H[:,2]))\n",
    "Pr = np.sqrt(np.square(Px)+np.square(Py))\n",
    "\n",
    "far_flux_circle = np.sum(Pr)*2*np.pi*r/len(Pr)\n",
    "\n",
    "print(\"flux:, {:.6f}, {:.6f}, {:.6f}\".format(near_flux,far_flux_box,far_flux_circle))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By Poynting's theorem, the total outgoing flux obtained by integrating around a closed surface should be the same whether it is calculated from the near or far fields (unless there are sources or absorbers in between). The flux of the near fields for the $J_z$ source is 2.456196 and that for the far fields is 2.458030 (box) and 2.457249 (circle). The ratio of near- to far-field (circle) flux is 0.999571. Similarly, for the $J_x$ source, the values are 1.227786 (near-field), 1.227651 (far-field box), and 1.227260 (far-field circle). The ratio of near-to far-field (circle) flux is 1.000429. The slight differences in the flux values are due to discretization effects and will decrease as the resolution is increased.\n",
    "\n",
    "Finally, we plot the radial flux normalized by its maximum value over the entire interval to obtain a range of values between 0 and 1. These are shown below in the linearly-scaled, polar-coordinate plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=200)\n",
    "ax = plt.subplot(111, projection='polar')\n",
    "ax.plot(angles,Pr/max(Pr),'b-')\n",
    "ax.set_rmax(1)\n",
    "ax.set_rticks([0,0.5,1])\n",
    "ax.grid(True)\n",
    "ax.set_rlabel_position(22)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
